Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4858
Title: Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector
Authors: Tumasyan, A.
Adam, W.
Andrejkovic, J.W.
Bergauer, T.
Chatterjee, S.
Damanakis, K.
Dragicevic, M.
Issue Date: 2023
Publisher: American Physical Society
Abstract: A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle Formula Presented into two photons, Formula Presented, is chosen as a benchmark decay. Lorentz boosts Formula Presented are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using Formula Presented decays in LHC collision data. © 2023 CERN, for the CMS Collaboration.
URI: https://doi.org/10.1103/PhysRevD.108.052002
https://hdl.handle.net/20.500.13091/4858
ISSN: 2470-0010
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Show full item record



CORE Recommender

SCOPUSTM   
Citations

2
checked on Feb 24, 2024

Page view(s)

12
checked on Feb 19, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.